TAMM: Tensor algebra for many-body methods

0573656 - ÚFCH JH 2024 RIV US eng J - Journal Article
Mutlu, E. - Panyala, A. - Gawande, N. - Bagusetty, A. - Glabe, J. - Kim, J. - Kowalski, K. - Bauman, N. P. - Peng, B. - Pathak, H. - Brabec, Jiří - Krishnamoorthy, S.
TAMM: Tensor algebra for many-body methods.
Journal of Chemical Physics. Roč. 159, č. 2 (2023), č. článku 024801. ISSN 0021-9606. E-ISSN 1089-7690
Institutional support: RVO:61388955
Keywords : tensor algebra operations * computational chemistry * Tensor algebra for many-body methods
OECD category: Physical chemistry
Impact factor: 4.4, year: 2022
Method of publishing: Open access

Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

Permanent Link: https://hdl.handle.net/11104/0344045